1. The Field of the Invention
The present invention relates to software, methods, and devices for evaluating correlations between observed phenomena and one or more factors having putative statistical relationships with such observed phenomena. More particularly, the software, methods, and devices described herein relate to the prediction of likely therapeutic outcomes for patients being treated with a therapeutic regimen.
2. Background
The application of statistical methods to the treatment of disease has been one of the great success stories of modem medicine. Using statistical methodologies, physicians and scientists have been able to identify sources, behaviors, and treatments for a wide variety of illnesses that have haunted humankind for centuries. Thus, for example, in the developed world, diseases such as cholera have been eradicated due in great part to the understanding of the causes of, and treatments for, these diseases using statistical analysis of the various risk and treatment factors associated with these diseases.
One particularly important application of statistical methods to medicine is the evaluation of the efficacy of regimens for treating diseases, and the use of statistical models to determine the likelihood of a particular patient's response to a treatment regimen. The latter application is especially important as treatment regimens for many diseases such as cancer, heart disease, and viral infections, including hepatitis B ("HBV"), hepatitis C ("HCV"), and acquired immune deficiency syndrome ("AIDS"), require a great deal of sacrifice on the part of the patient undergoing treatment in terms of cost, changes in lifestyle, and/or physical discomfort, with potentially problematic results. For example, therapy options for HBV are mostly limited to a course of interferon-.alpha. ("IFN.alpha.") treatments which have unpleasant side effects and are expensive. Indeed, some patients undergoing IFN.alpha. treatments for HBV are so burdened by the side effects of treatment they opt out of therapy entirely, even when the treatment is showing efficacy. In addition, only about one-third of those afflicted with HBV have a positive response to IFN.alpha. treatment (DiBisceglie, Fong et al. 1993). Even where the side effects of a treatment regimen are not so profound, statistical methods can be used to assist the physician and patient in evaluating treatment options. Thus, it is of great benefit for clinicians to have access to methods for evaluating the likelihood of the success of a treatment regimen before prescribing that regimen to a patient afflicted with a given disease.
In particular, HBV is a difficult disease to model. This difficulty is due at least in part to the highly complex nature of the interaction between HBV pathogen and its host. Aspects of this complex interaction include the variation of HBV levels in the host's blood stream during certain phases of the host's life cycle, the influence of the host's sex on HBV, the influence of the host's environment on HBV, and the interactions among HBV and other viruses that may infect the host such as AIDS or malaria (Coveney and Highfield; Blumberg 1994). Thus, any model for the prediction of therapeutic outcomes for HBV treatment will have to account for a variety of highly complex interactions within the virus-host system.
In general, the statistical methods used in medical applications have been limited to so-called logistic regression methods that relate clinical variables gathered from patients being treated for a disease with the probable treatment outcomes for those patients. Logistic regression methods are used to estimate the probability of defined outcomes as impacted by associated information. Typically, these methods utilize a sigmoidal logistic probability function (Dillon and Goldstein 1984) that is used to model the treatment outcome. The values of the model's parameters are determined using maximum likelihood estimation methods. The non-linearity of the logistic probability function, coupled with the use of the maximum likelihood estimation procedure, makes logistic regression methods complicated. Thus, such methods are often ineffective for complex models in which interactions among the various clinical variables being studied are present. In addition, the coupling of logistic and maximum likelihood methods limits the validation of logistic models to retrospective predictions which can overestimate the model's true abilities.
Logistic models can be combined with discriminant analysis to consider the interactions among the clinical variables being studied to provide a linear statistical model that is effective to discriminate among patient categories (e.g., responder and non-responder). Often these models comprise multivariate products of the clinical data being studied and utilize modifications of the methods commonly used in the purely logistic models. In addition, the combined logistic/discriminant models can be validated using prospective statistical methods in addition to retrospective statistical methods to provide a more accurate assessment of the model's predictive capability. However, these combined models are effective only for limited degrees of interactions among clinical variables and thus are inadequate for many applications.
Furthermore, both purely logistic and combined logistic/discriminant regression models are designed to correlate clinical variables, or products of clinical variables, with estimates of likely treatment outcome. Although the relationship between the clinical variables for a patient and the likely treatment outcome for that patient has utility, it will be appreciated that a clinician is more concerned with a patient than a set of clinical test results. Thus, the very basis on which traditional logistic regression commonly used in predicting therapeutic outcomes has to be questioned.
What is needed, therefore, are methods of providing statistically meaningful models for predicting likely treatment outcomes for specific treatment regimen that model the complex interactions among patient variables in a statistically robust manner. Moreover, there is a need for providing methods and systems that assist clinicians and patients in choosing a treatment regimen by providing both clinician and patient with a statistically meaningful estimation of the probability of a successful treatment outcome under the regimen being considered.